Systems and methods for medical acquisition processing and machine learning for anatomical assessment

ABSTRACT

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/001,767, filed Aug. 25, 2020, which is a continuation of U.S. patentapplication Ser. No. 15/852,183, filed Dec. 22, 2017, (now U.S. Pat. No.10,789,706), which claims priority to U.S. Provisional Application No.62/438,509, filed Dec. 23, 2016, each of which are incorporated hereinby reference in their entireties.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods fordetermining anatomy from raw medical acquisition.

BACKGROUND

Medical imaging is a powerful clinical tool for assessing the health ofa patient. Raw acquisition devices (e.g., computed tomography (CT),positron emission tomography (PET), single-photon emission computerizedtomography (SPECT), angiography, magnetic resonance imaging (MRI),ultrasound, etc.) may collect a significant amount of data using thetransmission and collection of energy and particles traveling throughand originating from the patient (e.g., in the form of x-rays,positrons, photons, ultrasonic waves, magnetic fields, contrast agent,etc.). Often, acquired data is reconstructed into an image. Thereconstructed image may be interpreted by a physician visually and/orusing image analysis software to assess the patient anatomy orphysiology.

However, information loss may occur during image reconstruction. Forexample, significant information loss may occur while transforming rawacquisition data to a set of discrete pixel intensity values. To accountfor this information loss, multiple different image reconstructions areoften utilized to assist a physician in performing an accurateassessment of the patient (e.g., the use of a sharp and softreconstruction kernel to assess, respectively, calcium and low densityplaque on cardiac computed tomography angiograph (CTA) imaging).Unfortunately, any image reconstruction may still involve an informationloss in transforming the raw acquisition to a set of images. Suchinformation loss may result in a suboptimal assessment of patienthealth, or patient anatomy and physiology. Accordingly, a desire existsto assess the anatomy and physiology of a patient, without theinformation loss caused by the transformation of raw acquisition data toimages.

SUMMARY

Systems and methods are disclosed for determining patient anatomy frommachine learning of raw medical acquisition data from the patient. Thedisclosed systems and methods offer a way of assessing a patient'sanatomy and physiology using raw data acquisitions versus imagereconstructions. The presently disclosed systems and methods offeradvantages in accuracy and processing speed over analysis of imagereconstructions to assess a patient's anatomy and physiology. One suchembodiment employs machine learning to predict measurements orparameters of a patient's anatomy and physiology. In one embodiment,disclosed machine learning systems and methods may involve two phases:first, a training phase in which a machine learning system is trained topredict anatomical parameters from raw acquisition data; and second, anapplication phase in which the machine learning system is used toproduce predicted anatomical parameters for a specific patient, usingonly a the raw measurement data from a medical acquisition associatedwith the patient. Analogous machine learning systems may be trained andapplied for various parameters in addition to anatomical parameters,e.g., physiological parameters, mechanical parameters, dynamicparameters, etc. Such embodiments are described in more detail at FIGS.2A and 2B.

According to one embodiment, a method is disclosed for determininganatomy from raw medical acquisition data of a patient. The methodincludes obtaining raw medical acquisition data from transmission andcollection of energy and particles traveling through and originatingfrom bodies of one or more individuals; obtaining a parameterized modelassociated with anatomy of each of the one or more individuals;determining one or more parameters for the parameterized model, whereinthe parameters are associated with the raw medical acquisition data;training a machine learning system to predict one or more values foreach of the determined parameters of the parametrized model, based onthe raw medical acquisition data; acquiring a medical acquisition for aselected patient; and using the trained machine learning system todetermine a parameter value for a patient-specific parameterized modelof the patient.

According to another embodiment, a system is disclosed for determininganatomy from a raw medical acquisition. The system includes a datastorage device storing instructions for determining anatomy from rawmedical acquisition data; and a processor configured to execute theinstructions to perform a method including the steps of: obtaining rawmedical acquisition data from transmission and collection of energy andparticles traveling through and originating from bodies of one or moreindividuals; obtaining a parameterized model associated with anatomy ofeach of the one or more individuals; determining one or more parametersfor the parameterized model, wherein the parameters are associated withthe raw medical acquisition data; training a machine learning system topredict one or more values for each of the determined parameters of theparametrized model, based on the raw medical acquisition data; acquiringa medical acquisition for a selected patient; and using the trainedmachine learning system to determine a parameter value for apatient-specific parameterized model of the patient.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for a method of determininganatomy from raw medical acquisition data is provided. The methodincludes: obtaining raw medical acquisition data from transmission andcollection of energy and particles traveling through and originatingfrom bodies of one or more individuals; obtaining a parameterized modelassociated with anatomy of each of the one or more individuals;determining one or more parameters for the parameterized model, whereinthe parameters are associated with the raw medical acquisition data;training a machine learning system to predict one or more values foreach of the determined parameters of the parametrized model, based onthe raw medical acquisition data; acquiring a medical acquisition for aselected patient; and using the trained machine learning system todetermine a parameter value for a patient-specific parameterized modelof the patient.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1A is a block diagram of an exemplary system and network fordetermining anatomy from a medical acquisition, according to anexemplary embodiment of the present disclosure.

FIG. 1B is a block diagram of an exemplary overview of a training phaseand an application phase for determining anatomy from raw medicalacquisition data, according to an exemplary embodiment of the presentdisclosure.

FIG. 2A is an exemplary method for training a machine learning system todetermine anatomy from raw medical acquisition data, according to anexemplary embodiment of the present disclosure.

FIG. 2B is an exemplary method for applying the trained machine learningsystem in order to determine anatomy from raw medical acquisition data,according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts. As usedherein, the term “exemplary” is used in the sense of “example,” ratherthan “ideal.”

As described above, raw patient data from medical acquisitions or imagesmay be reconstructed to facilitate analysis or interpretation byphysicians or image analysis software. However, information may be lostin the process of creating an image reconstruction from raw data. Thepresent disclosure describes systems and methods of assessing patientanatomy and/or physiology using the raw acquisition data, rather thanreconstructed images. In other words, the disclosed systems and methodsmay permit assessment of patient anatomy and/or physiology withoutcreating an image intermediary from raw data. In this way, theassessments of patient anatomy and/or physiology may take advantage ofthe full capacity of acquired raw data, rather than performingassessments on reconstructed images comprised of acquired data that hasundergone information loss. As described above, one exemplary system andmethod may include a training phase and an application phase (sometimesreferred to as “production phase”). During the training phase, a machinelearning system may be trained to predict patient-specific modelparameter values. During the application phase, the trained model may beused to determine patient-specific model parameter value(s) from apatient acquisition.

The training phase may be executed in a number of ways. In one generalform, the training phase may include receiving cases of raw acquisitiondata (e.g., k-space data from a T1-weighted magnetic resonance (MR)acquisition) and receiving expected output values associated with theraw acquisition data (e.g., the expected size or geometry of anindividual's Hippocampus). The training phase may include training amachine learning system to learn the relation between the input (e.g.,the raw acquisition data) and the output (e.g., parameter valuesdescribing the size/geometry of the hippocampus). The learned function“H” may be described as,Expected Output Value=H(raw acquisition data)

The training phase may also employ elements in addition to the machinelearning system. These additional elements may simplify or facilitatethe training of the machine learning system. For example, in one case,input may again include receiving expected output values associated withthe raw acquisition data (e.g., the expected size or geometry of anindividual's Hippocampus). However, the machine learning system trainingmay be aided with an image analysis method, including knowledge of therelationship between raw acquisition data and a reconstructed image. Theimage analysis method may provide expected output values (e.g.,parameter values describing the size/geometry of a hippocam pus) whengiven the input of reconstructed images (from the raw acquisition data).Since the input in the present embodiment includes raw acquisition datarather than reconstructed images, the present embodiment may includesimulating a reconstructed image from the raw acquisition data. Themachine learning system may then be trained to find the relation betweenthe simulated reconstructed image and the output (e.g., parameter valuesdescribing the size/geometry of the hippocampus). In this way, ratherthan the machine learning system having to learn the direct relationbetween raw acquisition data and anatomy (e.g., Expected OutputValue=H(raw acquisition data)), the machine learning system may find therelationship between simulated reconstructed images and raw acquisitiondata, since the relation between reconstructed images and anatomy may beprovided by the image analysis method. In other words, once the machinelearning system understands the relation between simulated reconstructedimages and the raw acquisition data, the output (e.g., parameter valuesdescribing the size/geometry of a hippocampus may be provided by theimage analysis method. For example, the image analysis method may bereferred to as method (“function S”), and finding a reconstructionmethod (“function R”) to generate a reconstructed image from the rawacquisition data that may provide expected outputs may be described as,Expected Output Value=S(R(raw acquisition data))

This embodiment may facilitate the first, general embodiment in that thefunction H is broken into functions S and R, e.g.,H(raw acquisition data)=S(R(raw acquisition data))

In addition, the machine learning training may be focused on learningfunction R, since function S may be provided as the image analysismethod. In one embodiment, the reconstruction method R used for thesimulated reconstructed image may be dictated by the expected outputs ofthe image analysis method, which may provide the relationship betweenreconstructed images and expected parameter values. For example,multiple reconstruction methods may be possible for a given set of rawacquisition data. The simulated reconstruction used for the machinelearning system training may be chosen from the simulated reconstructionof raw acquisition data that most closely provides the expected outputgiven by the image analysis method. For instance, generating thesimulated reconstructed image for machine learning system training mayinclude iteratively reconstructing the raw acquisition data either untilthe expected output associated with the simulated reconstruction matchesthe expected output of the image analysis method, or until no furtherimprovement may be made to matching the expected output associated withthe simulated reconstruction with the expected output of the imageanalysis method. No further improvement may mean that, after severaliterations, multiple reconstructions generate expected output that isequally close to matching the expected output of the image analysismethod. Each of the reconstructions may then be comparable for use asthe simulated reconstruction of the machine learning system training.

Alternately or in addition, determining patient anatomy from raw medicalacquisition data may involve prior information, e.g., information ondata acquisition. The process may be executed absent or with minimalmachine learning. For example, the input may again include receivingexpected output values associated with the raw acquisition data (e.g.,the expected size or geometry of an individual's Hippocampus). Furtherinput may include knowledge of how an acquisition works, including therelation between model parameters and acquisition data. For instance, asystem may understand that hippocampus of size x may correspond to ak-space measurement of y, simply due to knowledge of how acquisitionswork, and not from anatomical data related to individuals or patients.

For the application phase in a process that does not necessarily entailprior anatomical data related to individuals or patients, one method mayinclude iteratively modifying generic parametric model values until asimulated acquisition corresponding to a modified form of the genericparametric model corresponds to (or matches) the patients' raw medicalacquisition data. For example, the method may include initiating aprocess for determining patient anatomy from raw medical acquisitiondata by starting with model parameters of an average individual, ormodel parameters based on some input associated with a patient (e.g.,patient weight). Given the model parameters, acquisition data may besimulated from the prior knowledge of how acquisitions work. The modelparameter values may be iteratively changed until the simulatedacquisition data matches the actual measurement from a patient as wellas possible (e.g., until no further improvement may be made to thematch). In this case, the method may not include receiving prioranatomical data/expected outputs from individuals or patients. Rather,the method may focus on the relation between raw acquisitions and modelparameter values.

Referring now to the figures, FIG. 1A depicts a block diagram of anexemplary system and network for determining anatomy from a medicalacquisition. Specifically, FIG. 1A depicts a plurality of physicians 102and third party providers 104, any of whom may be connected to anelectronic network 100, such as the Internet, through one or morecomputers, servers, and/or handheld mobile devices. Physicians 102and/or third party providers 104 may create or otherwise obtain imagesof one or more patients' cardiac and/or vascular systems. The physicians102 and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, etc. Physicians 102 and/or third partyproviders 104 may transmit the cardiac/vascular images and/orpatient-specific information to server systems 106 over the electronicnetwork 100. Server systems 106 may include storage devices for storingimages and data received from physicians 102 and/or third partyproviders 104. Sever systems 106 may also include processing devices forprocessing images and data stored in the storage devices. Alternativelyor in addition, the present disclosure (or portions of the system andmethods of the present disclosure) may be performed on a localprocessing device (e.g., a laptop), absent an external server ornetwork.

FIG. 1B is a diagram of an overview 110 of an exemplary training phase111 and an exemplary application phase 121 for determining anatomy fromraw medical acquisition data, according to an exemplary embodiment ofthe present disclosure. In one embodiment, the training phase 111 mayinvolve generating associations between model parameters and raw datafrom the acquisition (step 117). Exemplary model parameters may includevessel centerline, plaque location coordinates or composition, contrastagent concentration level, motion coordinates of an anatomical point,blood flow at a particular anatomical location, etc. The applicationphase 121 may then use the associations to determine medical datarelated to a given patient. For example, one embodiment of theapplication phase 121 may include using the associations to determinemodel parameter values for a given patient, when provided with raw dataassociated with the patient. In one embodiment, the model parametervalues for a given patient may include a description or representationof the patient's anatomy. For example, the model parameter values mayinclude a collection of labeled surfaces describing a boundary of ananatomical structure, boundaries between different anatomicalstructures, or a combination thereof. Alternately or in addition, oneembodiment of the application phase 121 may include using theassociations to determine a representation of the patient's anatomycomprising a discrete grid with an anatomical label for each grid point.

In one embodiment, the training phase 111 may include receiving imagedata (e.g., training acquisition data 113) and a parameterized model(e.g., anatomic model 115). Training acquisition data 113 may includeraw data from any known medical imaging modality (e.g., CT, MR, SPECT,etc.). Training acquisition data 113 may further include any medicalacquisition that may involve a graphic display (e.g., a sinogram of a CTsystem, echo measurements of receivers or Doppler measurements ofreceivers of an ultrasound device, electric field measurements fromcardiac polarization wave imaging, measurements of wearable sensors,etc.). In one embodiment, parameterized anatomic model 115 may include2-D, 3-D, or other geometric models of human anatomy. Alternately or inaddition, parameterized anatomic model 115 may include parameterizedmodels of physiology, composition, response, dynamics, etc., asdescribed in detail in FIG. 2A. In other words, training acquisitiondata 113 may include clinical or medical data (e.g., image data) relatedto anatomical structures, function, or characteristics of parameterizedmodel 115. Training acquisition data 113 may further be paired withanatomy/physiology/characteristics represented by parameterized anatomicmodel 115.

In one embodiment, the training acquisition data 113 and parameterizedanatomic model 115 may be obtained from the same patient for whompatient-specific model parameter values are to be determined in anapplication phase. For example, one patient may have a machine learningsystem trained on his/her image/acquisition data. That machine learningsystem may be used to produce patient-specific model parameter valuesfor the patient. Alternately or in addition, training acquisition data113 and parameterized anatomic model 115 may be obtained from at leastone individual, other than the patient. The data may be collected from aplurality of individuals, from literature, computed, simulated, etc. Insuch a case, the machine learning system trained on data of severalindividuals other than a patient may still be applied to patient imagedata in the application phase to predict patient-specific modelparameter values. Other embodiments include using both patient data andindividual data to train the machine learning system. For example, afteran application phase, the patient image data of the application phasemay be used as input to the training phase, to supplement the trainingof the machine learning system. That machine learning system may then beapplied to predict patient-specific model parameters for anotherpatient, or for the same patient at a different point in time.

In one embodiment, associations 117 may be created between the receivedtraining acquisition data 113 (e.g., image data) and the anatomy,physiology, and/or characteristics represented by parameterized anatomicmodel 115. For example, data acquisitions including echo measurementscan provide information about a parameter involving the velocity ofblood flow. As another example, a data acquisition including a sinogramof a CT system may provide information about geometric parameters of aportion of anatomy, or the presence of a geometric parameter related toanatomy (e.g., plaque). In one embodiment, a machine learning system 119may store the associations 117 to predict future model parameter(s).

Machine learning system 119 may be used as an input to an exemplaryapplication phase 121, where patient-specific parameter values may bedetermined. In one embodiment, application phase 121 may includeobtaining a medical acquisition 123 of a patient of interest. Oneembodiment may also include obtaining image acquisition information 125(e.g., image acquisition parameters) associated with medical acquisition123. For example, a medical acquisition 123 including image data may beassociated with image acquisition parameters 125. The image acquisitionparameters may describe various imaging settings or patientphysiological state(s) during the generation of the medical acquisition123.

Machine learning system 119 may be applied to the received medicalacquisition 123 (and acquisition information or parameter(s) 125) todetermine a patient parameter value 127. In one embodiment, the patientparameter value 127 may include a parameter value of a model (e.g., ananatomical model, a motion model, a blood flow model, etc.). Theapplication phase 121 may further include generating a display 129including a representation of the model, or a representation based onthe patient parameter value 127. The representation may include asurface model, graph, chart, color-coding, indicators, interactivedisplays, alerts, signals, or any known graphical objects. Variousembodiments of graphical representations and displays are disclosed, forexample, in U.S. Pat. No. 8,706,457 issued Apr. 22, 2014, entitled“Method and System for Providing Information from a Patient-SpecificModel of Blood Flow,” which is incorporated by reference in itsentirety.

FIGS. 2A and 2B depict flowcharts of training and applying a machinelearning system to determine anatomy from raw medical acquisition data.In particular, FIG. 2A is a flowchart of an exemplary method 200 oftraining a machine learning system to determine anatomy from images.FIG. 2B is a flowchart of an exemplary method 210 for using the trainedmachine learning system to predict the structure of a particularpatient's anatomy. The methods of FIGS. 2A and 2B may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100.

FIG. 2A is a flowchart of an exemplary method 200 for training a machinelearning system to determine anatomy from raw medical acquisition data,according to an exemplary embodiment of the present disclosure. In oneembodiment, step 201 may include receiving, using an electronic storagemedium, one or more medical acquisitions of an individual orindividuals. The medical acquisitions may include raw acquisition databased on transmission and collection of energy and particles travelingthrough and originating from the individuals' body (e.g., x-ray,positrons, photons, ultrasonic waves, magnetic fields, etc.). Forexample, one medical acquisition may be comprised of a k-spaceacquisition corresponding to an magnetic resonance signal and anothermedical acquisition may be comprised of a sinogram associated with x-raybeam attenuation. In one embodiment, the medical acquisition may includea sinogram of a CT system, a K-space acquisition of an MRI system,magnetic relaxation measurement(s) (e.g., by one or more coils of aparallel MRI system), echo measurement(s) of receivers (e.g., on anultrasound device), Doppler measurement(s) of receivers (e.g., on anultrasound device), electric field measurement(s) from an electric fieldimaging system (e.g., for cardiac polarization wave imaging), video(s)or photograph(s) of the individual's anatomy/physiology, data fromwearable sensor(s) (e.g., including point, area, or volumemeasurement(s) of the sensors, time series acquisition(s) of any of theabove, etc. In one embodiment, step 201 may further include receiving aset of acquisition parameters. Exemplary acquisition parameters include:timing and/or location of a contrast agent injection, X-ray doseinformation, Gantry speed, patient preparation (e.g., dosage and timingof beta blockers or nitrates), etc.

In one embodiment, step 203 may include receiving at least oneparameterized model (mathematical, statistical, geometric, etc.) of atleast part of the individual's or individuals' anatomy. Anatomy mayinclude any structure of the individual's body, including vesselmorphology, organ size and shape, vessel lumen geometry, etc. Otherembodiments of step 203 may include (also or further) receiving at leastone parametrized model of the individual's physiology, composition,response, or dynamics thereof. In one embodiment, models of physiology,composition, response, or dynamics may also be considered “anatomicalmodels.” Physiology may include any functions of the individual's body,including mechanical or biochemical functions of the individual andhis/her organs. Composition may include a chemical make-up of theindividual's anatomy or anatomical function, including plaquecomposition, blood viscosity, organ density, etc. Response may includethe body's reaction to various stimuli, and dynamics may includemeasurements for density, composition, elasticity, concentration of atracer or contrast agent, etc.

Examples of a parameterized model may include: a vascular modelparameterized, for example, by vessel centerline location coordinates,where each centerline location may be additionally parameterized by avessel radius and/or a blood pool density, a disease model parameterizedby the location coordinates, volume, and/or density of calcified orlow-density plaque, an organ and/or tissue model (e.g., a model of apatient's liver, kidney, spleen, brain, heart, bone, prostate, breast,lung, knee, fat, water, etc.) parameterized, for example, by thelocation coordinates of at least two surface points in which eachsurface point may be associated with a set of neighboring points (e.g.,multiple times may be associated with different locations and/orneighbors for one or more surface points), a perfusion model in whichthe perfusion of blood or contrast agent may be parameterized as aconcentration level, coordinate location, and/or time, a motion modelthat may associate vector of motion coordinates with one or moreanatomical points, and/or a blood flow model that may associate a bloodflow characteristic (e.g., pressure, velocity, etc.) with one or morelocation coordinates.

In one embodiment, step 205 may include determining a type of parameterof the parameterized model (of step 203) that can be determined from themedical acquisition (of step 201). For example, given a parameterizedblood flow model, a medical acquisition of magnetic relaxationmeasurements may indicate a parameter value related to blood compositionor the presence of a blood clot. In such a case, blood composition maybe a parameter “type,” and presence of blood clot may be anotherparameter value “type.” As another example, given an anatomical motionmodel, a medical acquisition of Doppler measurements may indicate avalue related to tissue motion or velocity. As such, additionalexemplary parameter types may include “tissue motion” or “velocity.”Step 205 may include discerning types of parameter values related to theparametrized model (of step 203) that may be ascertained or estimatedfrom the medical acquisition (of step 201).

In one embodiment, step 207 may include using a computational device(e.g., computer, laptop, DSP, smart phone, tablet, GPU, etc.) to train amachine learning system to predict model parameter(s) values of thedetermined type of parameter, based on the parameterized model and theone or more medical acquisitions. For example, if the determinedparameter type (e.g., from step 205) is “tissue velocity,” step 207 mayinclude determining a value of tissue velocity in centimeters persecond, given a certain Doppler measurement input from an ultrasounddevice. As another example, if the determined parameter type is“presence of a blood clot,” the machine learning system may be trainedto predict “yes” or “no,” based on various magnetic relaxationmeasurements by a parallel MRI system. In yet another example, if thedetermined parameter type is a value of thickness of the left ventriclemyocardium, step 207 include determining the value of thickness directlyfrom a cardiac CT sinogram. Further, if the determined parameter type isa value indicating the size of a hippocampus, step 207 may includedetermining the value directly from the k-space measurement of aT1-weighed brain MRI scan.

In one embodiment, method 200 may include receiving a plurality ofmedical acquisitions and training the machine learning system using theplurality of acquisitions (step 207). The plurality of medicalacquisitions may be obtained from one individual, a group of individualswithin a single demographic (e.g., a defined patient population,hospital, age group, geographical region, etc.), or a patient ofinterest at a given point of time. Acquisitions used to train themachine learning system may comprise a “training set.” Multiple machinelearning techniques may be used for this training. Exemplary machinelearning techniques for training the model parameter machine learningsystem include: estimating the conditional probabilities and priorprobability for a Bayesian method, random forests, k-nearest neighbors,k-means, backpropagation, deep learning, multilayer perceptrons,logistic regression, linear regression, manifold learning techniques(e.g., locally linear embedding, isomap, etc.), semi-supervised learningtechniques (e.g., if training acquisitions are available withoutcorresponding patient models), etc. For example, given a parameterizedmodel of tumor growth and a plurality of longitudinal (long term)time-series k-space acquisitions of patient tumor progression, a randomforest may be trained to regress tumor size. Alternately or in addition,the training may employ methods that may not involve machine learning.For example, given a parameterized cardiac motion model and a pluralityof CT sinograms with associated values for cardiac motion modelparameters, a convolutional neural network may be trained viabackpropagation to minimize a loss function describing the differencebetween predicted model parameter values and parameter values of a givenmodel.

In one embodiment, step 209 may include storing results of the machinelearning system including predicted parameter value(s). In oneembodiment, a machine learning system may be adapted for each type ofmedical acquisition obtained. For example, each acquisition modality mayhave a designated machine learning system, e.g., a system for CT data, asystem for Doppler measurements, a system for angiography data, and asystem for wearable sensor data, etc. In another embodiment,acquisitions may be categorized by type. For instance, one machinelearning system may be trained on acquisitions of anatomy (e.g., CTscans, angiography data, volume measurements, etc.), while anothermachine learning system may be trained on acquisitions of physiology(e.g., blood flow or blood pressure measurements, perfusion data,wearable sensor data, etc.). In yet another embodiment, a single machinelearning system may provide predictions of parametric model parametersfor any medical acquisition.

FIG. 2B is a flowchart of an exemplary method 210 for training a machinelearning system to determine anatomy from raw medical acquisition data,according to an exemplary embodiment of the present disclosure. Themethod of FIG. 2B may be performed by server systems 106, based oninformation received from physicians 102 and/or third party providers104 over electronic network 100.

In one embodiment, step 211 may include receiving a medical acquisitionof a patient. In one embodiment, the patient may be a person other thanthe individual or group of individuals. Alternately or in addition, themedical acquisition may be obtained from the patient at a point of timelater than any patient medical acquisitions used for the training set.

In one embodiment, step 213 may include selecting a machine learningsystem associated with the type of medical acquisition of the patientmedical acquisition. For example, if step 211 includes receiving a CTscan, step 213 may include selecting a machine learning system trainedusing, at least in part, CT acquisition data. In an alternateembodiment, the medical acquisition of the patient may be obtained,depending on the machine learning systems available. For example, if amachine learning system based on CT acquisition data is available, ahealth care professional may elect to acquire a CT scan for a patient.In another instance, an angiography machine learning system may beavailable, but trained on less data than the CT scan machine learningsystem. In such a case, the health care professional may still opt foracquiring the CT scan, since the CT machine learning system may be morereliable than the angiography machine learning system.

In one embodiment, step 215 may include using a computational device(e.g., computer, laptop, DSP, smart phone, tablet, GPU, etc.), todetermine a set of parameter values for a patient-specific model usingthe trained machine learning system (e.g., of method 200). In otherwords, step 215 may include generating or computing a patient-specificmodel, using a trained machine learning system. In one embodiment, step215 may include determining one or more patient-specific model parametervalues based on the machine learning system (of method 200) and thereceived patient medical acquisition (of step 211). Alternately or inaddition, step 215 may include determining acquisition parameter(s)associated with the received medical acquisition of step 211 andprojecting the input acquisition parameter(s) to a learned manifold of amachine learning system of method 200 in order to computepatient-specific model parameter values. In yet another embodiment, step215 may include using Bayes' rule to calculate an estimated probabilitydistribution of a patient-specific model parameter value using themachine learning system of method 200 and the patient medicalacquisition of step 211. In such an embodiment, step 215 may furtherinclude selecting a set from the probability distribution (e.g., usingmaximum likelihood or maximum a posteriori). For example, if manytraining examples (e.g., sets of acquisition and target values) areavailable, one could choose to use a machine learning method with manydegrees of freedom, for example an artificial neural network. If only afew training examples are available, step 215 may employ moretraditional methods, e.g., support vector machines.

Step 215 of generating or computing the patient-specific model may besupplemented with prior information on how raw acquisition datameasurements may correlate to the anatomy or physiology of the patient.(e.g. prior information or raw data acquisition priors associated withCT physics may indicate how anatomical information relates to themeasured acquisition data). For example, a reconstructed image of theraw medical acquisition of the patient may be used as additional inputfor the machine learning system (alongside the raw data). As anotherexample, step 215 may include learning how to reconstruct an image fromthe medical acquisition of the patient, such that the reconstruction maybe optimal for a predefined image analysis technique that may providemodel parameter value(s) of the patient. In one embodiment, step 217 mayinclude outputting the determined patient-specific model parametervalues to an electronic storage device. One output may include, forexample, a display including a representation of patient anatomy orphysiology based on the determined patient-specific model parametervalues. Another exemplary output may include determining one or moreadditional properties of a patient anatomy and/or physiology using thedetermined patient-specific model parameter values. Additionalproperties may include, for example, organ/tissue volume, organ/tissueshape, vessel cross-sectional size and/or shape, organ/tissue surfacearea and/or shape, stenosis size, tumor/lesion presence or absence,tumor/lesion location, ejection fraction, stroke volume, a blood flowcharacteristic, plaque burden, calcium score, plaque vulnerability,tissue viability, myocardial wall motion, nuchal translucency, presenceor absence of patient pathology, valvular regurgitation, and/or patientdiagnosis.

In one embodiment, step 219 may further include prompting an update tothe stored machine learning system based on the received patient medicalacquisition (e.g., of step 211), the determined patient-specific modelparameter values, and/or input on the determined patient-specific modelparameters. Input on the determined patient-specific model parametersmay include user feedback on the patient-specific model parametervalues, including selection, verification, or modification of thevalues.

In a further embodiment, output patient-specific model parametervalue(s) (e.g., of step 217) may be compared to parameter(s) ofreconstructions based on the raw medical acquisition data. For example,image reconstruction(s) may be generated based on the raw medicalacquisition data. In some cases, the image reconstruction(s) may includerectilinear reconstructions. Anatomic models may also be generated basedon the image reconstruction(s). In one embodiment, patient-specificmodel parameter value(s) generated directly from the raw medicalacquisition data may be compared to image reconstruction(s) generatedfrom the raw medical acquisition data, or compared to anatomic model(s)generated from the image reconstruction(s). The comparison may be usedto generate confidence metrics in the image reconstruction(s) oranatomic model(s) generated from the image reconstruction(s). Forexample, the image reconstruction(s) or anatomic model(s) generated fromthe image reconstructions may be validated based on determinedpatient-specific model parameter value(s) (e.g., of step 217). Oneembodiment may include selecting an image reconstruction or anatomicmodel of a patient, based on determined patient-specific model parametervalue(s) and using the image reconstruction or anatomic model fordiagnostic analyses (e.g., blood flow modeling/simulations, plaquerupture predictions, perfusion estimates, etc.). Various embodiments ofdiagnostic analyses using image reconstructions and anatomic models aredisclosed, for example, in U.S. Pat. No. 8,315,812 issued Nov. 20, 2012,entitled “Method and System for Patient-Specific Modeling of BloodFlow,” which is incorporated by reference in its entirety.

The presently disclosed systems and methods offer advantages overanalysis of image reconstructions to assess a patient's anatomy andphysiology, in that the full capacity of acquired raw data may be usedfor the assessments. Resulting assessments are less impacted bypotential errors or data loss introduced by image reconstruction. Thedisclosed systems and methods provide machine learning embodiments as away to assess a patient's anatomy and physiology from raw dataacquisitions. In particular, the disclosed systems and methods usemachine learning to predict measurements or parameters of a patient'sanatomy and physiology.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for determininganatomy from raw medical acquisition data, the method comprising:obtaining, via at least one processor, raw medical acquisition data fora patient, the raw medical acquisition data including data resultingfrom transmission and collection of energy and particles travelingthrough and originating from the patient's body; and determining, viathe at least one processor, based on the raw medical acquisition data,and with no imaging or reconstruction intermediary, at least one aspectof the patient's anatomy by performing one or more iterations that eachinclude: modifying or setting one or more parameters of a parameterizedmodel corresponding to at least a portion of the patient's anatomycaptured by the raw medical acquisition data; simulating an acquisitionof raw medical acquisition data from the parameterized model; anddetermining a correspondence between the simulated raw medicalacquisition data to the obtained raw medical acquisition data.
 2. Thecomputer-implemented method of claim 1, further comprising: using thedetermined at least one aspect of the patient's anatomy to define, viathe at least one processor, one or more parameters of a patient-specificparameterized model.
 3. The computer-implemented method of claim 1,wherein the at least one aspect of the patient's anatomy includes a sizeor geometry of at least a portion of the patient's anatomy.
 4. Thecomputer-implemented method of claim 1, further comprising: obtaining,via the at least one processor, data that includes a size or geometry ofat least one portion of the patient's anatomy; wherein: the determiningof the at least one aspect of the patient's anatomy is further based onthe obtained data; and the size or geometry of the at least one portionof the patient's anatomy included in the obtained data is separate fromthe determined at least one aspect of the patient's anatomy.
 5. Thecomputer-implemented method of claim 4, wherein determining the at leastone aspect of the patient's anatomy includes: determining, via the atleast one processor, based on the raw medical acquisition data, and withno imaging or reconstruction intermediary, a prediction of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data; and validating the determining of the one or moreaspects of the patient's anatomy based on a comparison of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data and the predicted size or geometry of the at leastportion of the patient's anatomy determined based on the raw medicalacquisition data.
 6. The computer-implemented method of claim 1, whereinfurther iterations are performed until modifying the one or moreparameters fails to improve the correspondence between the simulated rawmedical acquisition data to the obtained raw medical acquisition data.7. The computer-implemented method of claim 1, wherein the parameterizedmodel is initialized using one or more parameters of an averageindividual.
 8. The computer-implemented method of claim 1, furthercomprising: obtaining, via the at least one processor, one or morecharacteristic of the patient; and initializing the parameterized modelusing one or more parameters determined based on the obtained one ormore characteristic.
 9. The computer-implemented method of claim 1,further comprising: determining, via the at least one processor andbased on the determined at least one aspect of the patient's anatomy, atleast one characteristic of the patient that includes one or more of anorgan or tissue volume, an organ or tissue shape, a vesselcross-sectional size or shape, an organ or tissue surface area orsurface shape, a stenosis size, a tumor or lesion presence or absence, atumor or lesion location, an ejection fraction, a stroke volume, a bloodflow characteristic, a plaque burden, a calcium score, a plaquevulnerability, a tissue viability, a myocardial wall motion, a nuchaltranslucency, a presence or absence of patient pathology, a valvularregurgitation, or a patient diagnosis.
 10. A system for determininganatomy from raw medical acquisition data, comprising: at least onememory storing instructions; and at least one processor operativelyconnected to the at least one memory and configured to execute theinstructions to perform operations, including: obtaining raw medicalacquisition data for a patient, the raw medical acquisition dataincluding data resulting from transmission and collection of energy andparticles traveling through and originating from the patient's body; anddetermining, based on the raw medical acquisition data and with noimaging or reconstruction intermediary, at least one aspect of thepatient's anatomy by performing one or more iterations that eachinclude: modifying or setting one or more parameters of a parameterizedmodel corresponding to at least a portion of the patient's anatomycaptured by the raw medical acquisition data; simulating an acquisitionof raw medical acquisition data from the parameterized model;determining a correspondence between the simulated raw medicalacquisition data to the obtained raw medical acquisition data; andfurther iterations are performed until modifying the one or moreparameters fails to improve the correspondence between the simulated rawmedical acquisition data to the obtained raw medical acquisition data.11. The system of claim 10, wherein the operations further include:using the determined at least one aspect of the patient's anatomy todefine, via the at least one processor, one or more parameters of apatient-specific parameterized model.
 12. The system of claim 10,wherein the at least one aspect of the patient's anatomy includes a sizeor geometry of at least a portion of the patient's anatomy.
 13. Thesystem of claim 10, wherein: the operations further include obtainingdata that includes a size or geometry of at least one portion of thepatient's anatomy; the determining of the at least one aspect of thepatient's anatomy is further based on the obtained data; and the size orgeometry of the at least one portion of the patient's anatomy includedin the obtained data is separate from the determined at least one aspectof the patient's anatomy.
 14. The system of claim 13, whereindetermining the at least one aspect of the patient's anatomy includes:determining, based on the raw medical acquisition data and with noimaging or reconstruction intermediary, a prediction of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data; and validating the determining of the at least oneaspect of the patient's anatomy based on a comparison of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data and the predicted size or geometry of the at leastportion of the patient's anatomy determined based on the raw medicalacquisition data.
 15. The system of claim 10, wherein the parameterizedmodel is initialized using one or more parameters of an averageindividual.
 16. The system of claim 10, wherein the operations furtherinclude: obtaining one or more characteristic of the patient; andinitializing the parameterized model using one or more parametersdetermined based on the obtained one or more characteristic.
 17. Thesystem of claim 10, wherein the operations further include: determining,based on the determined at least one aspect of the patient's anatomy, atleast one characteristic of the patient that includes one or more of anorgan or tissue volume, an organ or tissue shape, a vesselcross-sectional size or shape, an organ or tissue surface area orsurface shape, a stenosis size, a tumor or lesion presence or absence, atumor or lesion location, an ejection fraction, a stroke volume, a bloodflow characteristic, a plaque burden, a calcium score, a plaquevulnerability, a tissue viability, a myocardial wall motion, a nuchaltranslucency, a presence or absence of patient pathology, a valvularregurgitation, or a patient diagnosis.
 18. A computer-implemented methodfor determining anatomy from raw medical acquisition data, the methodcomprising: obtaining, via at least one processor, raw medicalacquisition data for a patient, the raw medical acquisition dataincluding data resulting from transmission and collection of energy andparticles traveling through and originating from the patient's body; andobtaining, via the at least one processor, data that includes a size orgeometry of at least one portion of the patient's anatomy; determining,via the at least one processor, based on the raw medical acquisitiondata and the obtained data, and with no imaging or reconstructionintermediary, at least one aspect of the patient's anatomy, wherein: thesize or geometry of the at least one portion of the patient's anatomyincluded in the obtained data is separate from the determined at leastone aspect of the patient's anatomy; and determining the at least oneaspect of the patient's anatomy includes: determining, via the at leastone processor, based on the raw medical acquisition data, and with noimaging or reconstruction intermediary, a prediction of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data; and validating the determining of the at least oneaspect of the patient's anatomy based on a comparison of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data and the predicted size or geometry of the at leastportion of the patient's anatomy determined based on the raw medicalacquisition data.
 19. A system for determining anatomy from raw medicalacquisition data, comprising: at least one memory storing instructions;and at least one processor operatively connected to the at least onememory and configured to execute the instructions to perform operations,including: obtaining raw medical acquisition data for a patient, the rawmedical acquisition data including data resulting from transmission andcollection of energy and particles traveling through and originatingfrom the patient's body; obtaining, via the at least one processor, datathat includes a size or geometry of at least one portion of thepatient's atomy; and determining, based on the raw medical acquisitiondata and the obtained data and with no imaging or reconstructionintermediary, at least one aspect of the patient's anatomy, wherein: thesize or geometry of the at least one portion of the patient's anatomyincluded in the obtained data is separate from the determined at leastone aspect of the patient's anatomy; and determining the at least oneaspect of the patient's anatomy includes: determining, via the at leastone processor, based on the raw medical acquisition data, and with noimaging or reconstruction intermediary, a prediction of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data; and validating the determining of the at least oneaspect of the patient's anatomy based on a comparison of the size orgeometry of the at least portion of the patient's anatomy included inthe obtained data and the predicted size or geometry of the at leastportion of the patient's anatomy determined based on the raw medicalacquisition data.